11-CS-4 Engineering Law and Professional Liability · May 2019
Nivaar worked solution (AI-drafted; not reviewed by a licensed engineer)
National Exams — May 2019 — 11-CS-4 Engineering Management. Closed book. Any five questions constitute a complete paper; all questions are of equal value (20 marks each). Full worked answers to all eight questions are given below; part marks are from the printed marking scheme.
Question text not reproduced: the examination questions are © Engineers and Geoscientists BC. Open the official past paper (linked at the top of this page) to read the question, then follow the worked solution below.
Design for Manufacturability is the practice of designing a product so that it can be made easily, reliably, and economically, since most of a product's cost is committed at the design stage. The factors to consider include minimizing the number of parts, as every part adds tooling, assembly, and inventory cost and a potential failure point; standardizing components and materials so common, off-the-shelf items replace custom ones; and designing for ease of assembly, with self-locating features, symmetry or clear asymmetry to prevent misorientation, and tool access. Further factors are selecting materials and processes matched to the production volume, specifying realistic tolerances—tight tolerances raise cost sharply and should be relaxed where function permits—and designing for the chosen process (draft angles for moulding, adequate radii for casting, avoidance of secondary operations). Designers must also consider ease of inspection and testing, modularity for variety without complexity, and serviceability. Concurrent engineering, involving manufacturing and quality staff from the outset, is the mechanism that makes DFM effective.
Bringing a new product or service to market exposes the firm to technical, commercial, and financial risk, and several tools identify and quantify it. SWOT analysis frames internal strengths and weaknesses against external opportunities and threats. Failure Mode and Effects Analysis (FMEA) lists potential failure modes and rates each by severity, occurrence, and detectability into a Risk Priority Number that ranks what to mitigate first. Sensitivity analysis varies one uncertain input at a time to see how strongly the outcome responds, while scenario analysis examines best-, most-likely-, and worst-case futures together. Decision-tree analysis maps sequential choices and their probabilistic payoffs, and Monte-Carlo simulation draws thousands of random samples from input distributions to produce a probability distribution of the result rather than a single point estimate. A risk register and probability–impact matrix document each risk, its owner, and its response (avoid, reduce, transfer, or accept). Together these convert vague apprehension into quantified, prioritized, and manageable exposure.
Simulation modelling builds a computer model of a production process and runs it to imitate the system's behaviour over time, letting engineers study and improve the process without disturbing the real operation. In tracking production processes, a model fed with actual orders, cycle times, and breakdown records (and, in modern plants, linked live to shop-floor data as a "digital twin") follows each job, batch, or unit through the process and reports work-in-process, throughput, cycle time, lead time, and resource utilization, so planned and actual performance can be compared and emerging delays spotted early. In identifying production problems it is especially powerful. It exposes bottlenecks—the stations that constrain throughput—by revealing where work-in-process accumulates and queues form. It supports capacity and resource analysis, showing the utilization of machines and labour and the effect of adding or removing resources. It enables "what-if" experimentation—testing changes to layout, scheduling, batch sizes, or staffing safely and cheaply in the model before committing them to the plant. It captures variability and randomness (breakdowns, variable processing and arrival times) that static calculations miss, giving realistic estimates of throughput, cycle time, and queue lengths. And it helps diagnose specific problems—excessive waiting, blocking, or starvation—and validate proposed solutions before investment. By experimenting on the model rather than the real process, simulation reduces the cost and risk of finding and fixing production problems, and is widely used in manufacturing, logistics, and service operations.
A firm launching a new assembled product would apply DFM to cut part count and ease assembly, and run an FMEA plus a Monte-Carlo cost model to quantify launch risk. Before installing the line, it would build a discrete-event simulation that reveals a testing station as the bottleneck and shows, through "what-if" runs, that a second tester or a revised buffer removes the constraint—identifying and solving the production problem in the model rather than after costly installation.
